Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	18,250
2	New Jersey	16,474
3	Massachusetts	12,478
4	Rhode Island	11,963
5	Connecticut	10,495
6	District of Columbia	10,092
7	Delaware	7,876
8	Illinois	7,446
9	Louisiana	7,406
10	Maryland	6,437
11	Nebraska	5,349
12	Pennsylvania	5,135
13	Michigan	5,112
14	Iowa	4,643
15	South Dakota	4,506
16	Indiana	4,221
17	Colorado	3,804
18	Mississippi	3,795
19	Virginia	3,560
20	Georgia	3,386
21	New Mexico	2,850
22	Minnesota	2,779
23	Kansas	2,769
24	New Hampshire	2,644
25	Washington	2,573
26	Tennessee	2,546
27	North Dakota	2,493
28	Alabama	2,400
29	Ohio	2,388
30	Utah	2,257
31	Nevada	2,254
32	Wisconsin	2,159
33	Florida	2,122
34	California	2,033
35	Arizona	1,914
36	Missouri	1,803
37	North Carolina	1,767
38	Kentucky	1,744
39	South Carolina	1,712
40	Texas	1,683
41	Arkansas	1,576
42	Vermont	1,506
43	Idaho	1,354
44	Oklahoma	1,341
45	Wyoming	1,302
46	Maine	1,255
47	Oregon	858
48	West Virginia	832
49	Puerto Rico	828
50	Alaska	541
51	Hawaii	444
52	Montana	437

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	207
2	Massachusetts	185
3	Connecticut	182
4	District of Columbia	182
5	Illinois	165
6	Nebraska	161
7	Maryland	159
8	Delaware	153
9	New Jersey	136
10	Minnesota	132
11	New York	117
12	Iowa	103
13	Virginia	100
14	Mississippi	91
15	Indiana	88
16	North Dakota	82
17	New Mexico	75
18	South Dakota	73
19	Wisconsin	73
20	Louisiana	67
21	Pennsylvania	67
22	Colorado	63
23	North Carolina	61
24	Arizona	57
25	Michigan	52
26	New Hampshire	52
27	Utah	49
28	Georgia	48
29	Alabama	45
30	California	45
31	Kansas	45
32	Ohio	44
33	Arkansas	43
34	Texas	42
35	South Carolina	40
36	Tennessee	39
37	Florida	36
38	Nevada	35
39	Missouri	31
40	Maine	30
41	Wyoming	30
42	Kentucky	29
43	Oklahoma	29
44	Washington	27
45	Puerto Rico	22
46	Idaho	12
47	Oregon	11
48	West Virginia	10
49	Alaska	4
50	Vermont	4
51	Montana	1
52	Hawaii	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,447
2	New Jersey	1,165
3	Connecticut	955
4	Massachusetts	841
5	District of Columbia	542
6	Louisiana	521
7	Michigan	489
8	Rhode Island	471
9	Pennsylvania	351
10	Illinois	331
11	Maryland	329
12	Delaware	297
13	Indiana	260
14	Colorado	210
15	Mississippi	175
16	Georgia	149
17	Ohio	139
18	Washington	133
19	Minnesota	129
20	New Hampshire	126
21	New Mexico	126
22	Virginia	118
23	Nevada	113
24	Iowa	111
25	Alabama	99
26	Missouri	98
27	Arizona	93
28	Florida	91
29	Vermont	86
30	California	83
31	Kentucky	77
32	Wisconsin	77
33	South Carolina	74
34	Oklahoma	72
35	Nebraska	66
36	Kansas	64
37	North Carolina	64
38	North Dakota	56
39	Maine	52
40	South Dakota	49
41	Texas	46
42	Tennessee	42
43	Idaho	40
44	Puerto Rico	38
45	West Virginia	37
46	Arkansas	32
47	Oregon	32
48	Utah	24
49	Montana	14
50	Wyoming	13
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Connecticut	17
2	Massachusetts	15
3	New Jersey	15
4	District of Columbia	11
5	Delaware	10
6	New York	9
7	Rhode Island	9
8	Colorado	7
9	Illinois	6
10	Maryland	6
11	Indiana	5
12	Louisiana	5
13	New Hampshire	5
14	Pennsylvania	5
15	Mississippi	4
16	Iowa	3
17	Michigan	3
18	Minnesota	3
19	New Mexico	3
20	Arizona	2
21	California	2
22	Missouri	2
23	Nebraska	2
24	Ohio	2
25	Virginia	2
26	Alabama	1
27	Florida	1
28	Georgia	1
29	Nevada	1
30	North Carolina	1
31	North Dakota	1
32	Texas	1
33	Washington	1
34	Wisconsin	1
35	Alaska	0
36	Arkansas	0
37	Hawaii	0
38	Idaho	0
39	Kansas	0
40	Kentucky	0
41	Maine	0
42	Montana	0
43	Oklahoma	0
44	Oregon	0
45	Puerto Rico	0
46	South Carolina	0
47	South Dakota	0
48	Tennessee	0
49	Utah	0
50	Vermont	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	122,563	1	99
Dakota	Nebraska	75,452	2	99
Lincoln	Arkansas	73,326	3	99
Nobles	Minnesota	62,925	4	99
Lake	Tennessee	57,583	5	99
Davidson	Tennessee	5,953	253	91
Richland	South Carolina	2,978	602	80
Pierce	Washington	1,990	872	72
Orange	California	1,384	1170	62
York	South Carolina	1,043	1428	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	3,098	1	99
Terrell	Georgia	2,813	2	99
Early	Georgia	2,748	3	99
Essex	New Jersey	1,912	4	99
Nassau	New York	1,855	5	99
Richland	South Carolina	147	502	84
Pierce	Washington	75	800	74
Davidson	Tennessee	63	903	71
Orange	California	28	1273	59
York	South Carolina	18	1441	54

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.